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351,949
A kernel-based approach to direct action perception
- In IEEE International Conference on Robotics and Automation
, 2012
"... Abstract—The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the sim ..."
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Cited by 12 (2 self)
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Abstract—The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing
imagery with albedo-based and kernel-based approaches
"... intimate mixtures of materials in hyperspectral ..."
A Relational Kernel-based Approach to Scene Classification
"... Real-world scenes involve many objects that interact with each other in complex semantic patterns. For example, a bar scene can be naturally described as having a variable number of chairs of similar size, close to each other and aligned horizontally. This high-level interpretation of a scene relies ..."
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, today recent successes in combining them with statistical learning principles motivates us to reinvestigate their use. In this paper we show that relational techniques can also improve scene classification. More specifically, we employ a new relational language for learning with kernels, called k
A Kernel-based Approach to Document Retrieval
"... In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the prob-ability that a given document belongs to a certain class. The mem-bership probability to a specific class is computed using Support Vector Machines in conjunction with sim ..."
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Cited by 1 (0 self)
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with similarity measure based ker-nel applied to structural document representations. In the presented experiments, we use different document representations, both vi-sual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms
A Kernel Based Approach to Maximum Entropy Mappings
"... Abstract- We discuss a kernel based method for learning maximum entropy mappings from exemplars. Information theoretic signal processing has been ex-amined by many authors. The method presented here is related to the approaches of Linsker [l, 21, Bell and Sejnowski [3], and Viola et a1 [4]. In this ..."
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Abstract- We discuss a kernel based method for learning maximum entropy mappings from exemplars. Information theoretic signal processing has been ex-amined by many authors. The method presented here is related to the approaches of Linsker [l, 21, Bell and Sejnowski [3], and Viola et a1 [4
Y: A single kernel-based approach to extract drug-drug interactions from biomedical literature
- PLOS ONE
"... When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increas ..."
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Cited by 1 (0 self)
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it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information
Outlier robust system identification: a Bayesian kernel-based approach
"... Abstract: In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information ..."
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Cited by 1 (0 self)
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Abstract: In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information
A kernel-based approach to learning semantic parsers. Doctoral Dissertation Proposal
, 2005
"... Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing user-friendly natural language interfaces to computing systems. Most of the research in natural langu ..."
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Cited by 1 (0 self)
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to filling a single semantic frame. In this proposal, we present a new approach to semantic parsing based on string-kernel-based classification. Our system takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar
Results 1 - 10
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351,949
